The quality of an image captured by a digital camera can be influenced by factors including the exposure and focal plane settings of the camera and the dynamic range within a scene. The exposure (duration of time which light is sampled by pixels in an image sensor) impacts the color shades and tone and the focal plane settings impact a captured image's sharpness.
The dynamic range within a scene is the difference in brightness from the darkest to brightest sections of the scene. Likewise, the dynamic range of an image sensor is the difference in brightness from the darkest to brightest sections that the image sensor is able to capture. Depending on the dynamic range within a particular scene, the maximum dynamic range of an image sensor can be many times smaller than the scene's dynamic range. Thus, digital cameras may not be able to accurately capture the full range of brightness in any given scene. Various techniques including auto-exposure, autofocus and high dynamic range imaging have been developed to improve the quality of images captured using digital cameras.
In many image capture devices the sensitivity of the device to light intensity can be adjusted by manipulating pixel integration time, pixel gain, and/or iris/lens aperture. Further, metering and auto-exposure algorithms can be used to optimize the above parameters (some of these parameters may be specified or fixed). Auto-exposure algorithms utilize methods to capture images at optimal mean brightness levels by adjusting the exposure time (or focal plane settings). Such algorithms generally perform an iterative process that captures an image at a known exposure time and based on the characteristics of the captured image, sets the exposure time (or focal plane settings) to capture following images at more optimal mean brightness levels.
Most surfaces reflect incident light with some amount of scattering. Thus, the light intercepted by a camera is roughly isotropic with a small region around the vantage point of the camera. Thus, individual imaging components of an array camera should ideally provide the same numerical representation of an object in the individual images captured by each of the imaging components. However, non-idealities exist in an array camera and its individual imaging components due to manufacturing tolerances and other aberrations.
As such, the numerical representation for the same point in space as captured in the image data of each individual imaging component may differ. The differences may be subtle such as those differences caused by among other things, the differences in focal length, aperture ratios, and image sensor sensitivity in the individual imaging components. Some of these differences can be treated as constants and may be accounted for by correction factors determined through a calibration process.
However, there are some differences that are introduced by the scene being imaged that cannot be compensated for ahead of time by correction factors. One example is veiling glare. Veiling glare occurs when the image projected onto the pixels or sensors of an imaging component by a lens system includes the intended image and an erroneous internally scattered set of photons. The internally scattered set of photons may originate from anywhere in front of the imaging component including both within and outside the Field of View (FoV) of the imaging component. This causes the image projected onto the pixels or sensors of the imaging component at a given point to have more than or less than the expected photons. Additional non-idealities may also exist including, but not limited to, contaminants on a protective window over the array camera installed in a device. The contaminants may change the photo-response function for each of the individual imaging components by scattering or absorbing some of the photons entering the optical system.
It is a problem if the individual imaging components of the array camera do not report the same value for a given point in scene space in their image data. If the values for the same point in space differ in the image data of individual imaging components, the parallax detection between the different images may fail or become erroneous. Also, a noise signal may be introduced into fused images from the local differences in the numerical values of the image data from different imaging components.